Real-Time Detection of Distributed Network Security Risk Based on Kernel Fuzzy Clustering Algorithm

In order to improve the security of distributed network and reduce the operational risk of distributed network, a realtime detection method of distributed network security risk based on kernel fuzzy algorithm is proposed. According to the structure of the distributed network, the kernel fuzzy clustering algorithm is used to construct the objective function of the distributed network security risk data clustering, and complete the clustering processing of the security risk data. Based on the clustering results and according to the principle of immunity, the immune algorithm is used to build the security risk detector model to complete the realtime detection of security risk. The experimental results show that the proposed realtime detection method for security risk has more accurate detection accuracy and shorter detection time, with the average detection accuracy of 93.582% and the longest detection time of less than 1s.